Understanding nuclear receptors using computational methods

Profiling prediction of nuclear receptor modulators with multi-task deep learning methods: toward the virtual screening

Nuclear receptors (NRs) are ligand-activated transcription factors, which constitute one of the most important targets for drug discovery. Current computational strategies mainly focus on a single target, and the transfer of learned knowledge among NRs was not considered yet. Herein we proposed a novel computational framework named NR-Profiler for prediction of potential NR modulators with high affinity and specificity. First, we built a comprehensive NR data set including 42 684 interactions to connect 42 NRs and 31 033 compounds. Then, we used multi-task deep neural network and multi-task graph convolutional neural network architectures to construct multi-task multi-classification models. To improve the predictive capability and robustness, we built a consensus model with an area under the receiver operating characteristic curve (AUC) = 0.883. Compared with conventional machine learning and structure-based approaches, the consensus model showed better performance in external validation. Using this consensus model, we demonstrated the practical value of NR-Profiler in virtual screening for NRs. In addition, we designed a selectivity score to quantitatively measure the specificity of NR modulators. Finally, we developed a freely available standalone software for users to make profiling predictions for their compounds of interest. In summary, our NR-Profiler provides a useful tool for NR-profiling prediction and is expected to facilitate NR-based drug discovery.

Critical Insight into the Design of PPAR-γ Agonists by Virtual Screening Techniques

BACKGROUND

Design of novel PPAR-γ modulators with better binding efficiency and fewer side effects to treat type 2 diabetes is still a challenge for medicinal chemists. Cost and time efficient computational methods have presently become an integral part of research in nuclear receptors and their ligands, enabling hit to lead identification and lead optimization. This review will focus on cutting-edge technologies used in most recent studies on the design of PPAR- γ agonists and will discuss the chemistry of few molecules which emerged successful.

METHODS

Literature review was carried out in google scholar using customized search from 2011- 2017. Computer-aided design methods presented in this article were used as search terms to retrieve corresponding literature.

RESULTS

Virtual screening of natural product libraries is an effective strategy to harness nature as the source of ligands for PPARs. Rigid and induced fit docking and core hopping approach in docking are rapid screening methods to predict the PPAR- γ and PPAR-α/ γ dual agonistic activity. Onedimensional drug profile matching is one of the recent virtual screening methods by which an antiprotozoal drug, Nitazoxanide was identified as a PPAR- γ agonist.

CONCLUSION

It is concluded that to achieve a convincing and reliable design of PPAR-γ agonist by virtual screening techniques, customized workflow comprising of appropriate models is essential in which methods may be applied either sequentially or simultaneously.

Ligand Pathways in Nuclear Receptors

Nuclear receptors (NRs) are ligand-inducible transcription factors that play an essential role in a multitude of physiological processes as well as diseases, rendering them attractive drug targets. Crystal structures revealed the binding site of NRs to be buried in the core of the protein, with no obvious route for ligands to access this cavity. The process of ligand binding is known to be an often-neglected contributor to the efficacy of drug candidates and is thought to influence the selectivity and specificity of NRs. While experimental methods generally fail to highlight the dynamic processes of ligand access or egress on the atomistic scale, computational methods have provided fundamental insight into the pathways connecting the buried binding pocket to the surrounding environment. Methods based on molecular dynamics (MD) and Monte Carlo simulations have been applied to identify pathways and quantify their capability to transport ligands. Here, we systematically review findings of more than 20 years of research in the field, including the applied methodology and controversies. Further, we establish a unified nomenclature to describe the pathways with respect to their location relative to protein secondary structure elements and summarize findings relevant to drug design. Lastly, we discuss the effect of NR interaction partners such as coactivators and corepressors, as well as mutations on the pathways.

Exploring the PXR ligand binding mechanism with advanced Molecular Dynamics methods

The Pregnane X Receptor (PXR) is a ligand-activated transcription factor belonging to the nuclear receptor family. PXR can bind diverse drugs and environmental toxicants with different binding modes, making it an intriguing target for drug discovery. Here we investigated the binding mechanism of the SR12813 ligand to elucidate the significant steps, from the ligand entrance pathway into the binding cavity, to the ligand-induced conformational changes, and to the exploration of its alternative binding geometries. We used the advanced Molecular Dynamics-based methods implemented in the BiKi suite and developed specific methodological approaches to overcome the complexity induced by the buried and flexible binding cavity. The adopted methods provided a full dynamic description of the binding event and allowed rationalization of the observed multiple binding modes. These results suggest that the same approach could be exploited for the study of other binding processes with similar characteristics.

Insight into glucocorticoid receptor signalling through interactome model analysis

Glucocorticoid hormones (GCs) are used to treat a variety of diseases because of their potent anti-inflammatory effect and their ability to induce apoptosis in lymphoid malignancies through the glucocorticoid receptor (GR). Despite ongoing research, high glucocorticoid efficacy and widespread usage in medicine, resistance, disease relapse and toxicity remain factors that need addressing. Understanding the mechanisms of glucocorticoid signalling and how resistance may arise is highly important towards improving therapy. To gain insight into this we undertook a systems biology approach, aiming to generate a Boolean model of the glucocorticoid receptor protein interaction network that encapsulates functional relationships between the GR, its target genes or genes that target GR, and the interactions between the genes that interact with the GR. This model named GEB052 consists of 52 nodes representing genes or proteins, the model input (GC) and model outputs (cell death and inflammation), connected by 241 logical interactions of activation or inhibition. 323 changes in the relationships between model constituents following in silico knockouts were uncovered, and steady-state analysis followed by cell-based microarray genome-wide model validation led to an average of 57% correct predictions, which was taken further by assessment of model predictions against patient microarray data. Lastly, semi-quantitative model analysis via microarray data superimposed onto the model with a score flow algorithm has also been performed, which demonstrated significantly higher correct prediction ratios (average of 80%), and the model has been assessed as a predictive clinical tool using published patient microarray data. In summary we present an in silico simulation of the glucocorticoid receptor interaction network, linked to downstream biological processes that can be analysed to uncover relationships between GR and its interactants. Ultimately the model provides a platform for future development both by directing laboratory research and allowing for incorporation of further components, encapsulating more interactions/genes involved in glucocorticoid receptor signalling.

Here we present modelling of the glucocorticoid receptor (GR) signalling network. The GR is the effector for a class of drugs known as corticosteroids, which are widely used in medicine for their anti-inflammatory effects and ability to induce apoptosis in leukaemic cells. However, side effects, treatment-related toxicity and glucocorticoid resistance remain and therefore increased understanding of the glucocorticoid receptor mechanism of action may improve therapeutic outcomes. The GEB052 model presented herein has been used to generate predictions for how the network is altered between glucocorticoid-sensitive and glucocorticoid-resistant scenarios, and these predictions have been verified using published gene expression data from established cell lines (for both qualitative and semi-quantitative analysis). The model has also been preliminarily assessed as a predictive clinical tool by correlating model predictions with clinical outcomes of thirteen leukaemia patients. Thus, the GEB052 model demonstrates successful modelling to understand GR function. GEB052 provides accurate predictions and has indicated potential routes through which glucocorticoid resistance may arise. The work presented herein thus demonstrates a proof-of-principle of this modelling approach to furthering GR research, and provides insight into potential mechanisms of corticosteroids resistance.

In silico methods for predicting drug-drug interactions with cytochrome P-450s, transporters and beyond

Drug-drug interactions (DDIs) are associated with severe adverse effects that may lead to the patient requiring alternative therapeutics and could ultimately lead to drug withdrawal from the market if they are severe. To prevent the occurrence of DDI in the clinic, experimental systems to evaluate drug interaction have been integrated into the various stages of the drug discovery and development process. A large body of knowledge about DDI has also accumulated through these studies and pharmacovigillence systems. Much of this work to date has focused on the drug metabolizing enzymes such as cytochrome P-450s as well as drug transporters, ion channels and occasionally other proteins. This combined knowledge provides a foundation for a hypothesis-driven in silico approach, using either cheminformatics or physiologically based pharmacokinetics (PK) modeling methods to assess DDI potential. Here we review recent advances in these approaches with emphasis on hypothesis-driven mechanistic models for important protein targets involved in PK-based DDI. Recent efforts with other informatics approaches to detect DDI are highlighted. Besides DDI, we also briefly introduce drug interactions with other substances, such as Traditional Chinese Medicines to illustrate how in silico modeling can be useful in this domain. We also summarize valuable data sources and web-based tools that are available for DDI prediction. We finally explore the challenges we see faced by in silico approaches for predicting DDI and propose future directions to make these computational models more reliable, accurate, and publically accessible.

Identifying farnesoid X receptor agonists by naïve Bayesian and recursive partitioning approaches

For the first time, NB and RP were successfully employed to predict FXR agonists. Two new FXR agonists were identified with the models, and confirmed with cell-based experiments.

Allosteric mechanisms of nuclear receptors: insights from computational simulations

The traditional structural view of allostery defines this key regulatory mechanism as the ability of one conformational event (allosteric site) to initiate another in a separate location (active site). In recent years computational simulations conducted to understand how this phenomenon occurs in nuclear receptors (NRs) has gained significant traction. These results have yield insights into allosteric changes and communication mechanisms that underpin ligand binding, coactivator binding site formation, post-translational modifications, and oncogenic mutations. Moreover, substantial efforts have been made in understanding the dynamic processes involved in ligand binding and coregulator recruitment to different NR conformations in order to predict cell/tissue-selective pharmacological outcomes of drugs. They also have improved the accuracy of in silico screening protocols so that nowadays they are becoming part of optimisation protocols for novel therapeutics. Here we summarise the important contributions that computational simulations have made towards understanding the structure/function relationships of NRs and how these can be exploited for rational drug design.

Proteomic mechanisms of cardioprotection during mammalian hibernation in woodchucks, Marmota monax

Mammalian hibernation is a unique strategy for winter survival in response to limited food supply and harsh climate, which includes resistance to cardiac arrhythmias. We previously found that hibernating woodchucks (Marmota monax) exhibit natural resistance to Ca2+ overload-related cardiac dysfunction and nitric oxide (NO)-dependent vasodilation, which maintains myocardial blood flow during hibernation. Since the cellular/molecular mechanisms mediating the protection are less clear, the goal of this study was to investigate changes in the heart proteome and reveal related signaling networks that are involved in establishing cardioprotection in woodchucks during hibernation. This was accomplished using isobaric tags for a relative and absolute quantification (iTRAQ) approach. The most significant changes observed in winter hibernation compared to summer non-hibernation animals were upregulation of the antioxidant catalase and inhibition of endoplasmic reticulum (ER) stress response by downregulation of GRP78, mechanisms which could be responsible for the adaptation and protection in hibernating animals. Furthermore, protein networks pertaining to NO signaling, acute phase response, CREB and NFAT transcriptional regulations, protein kinase A and α-adrenergic signaling were also dramatically upregulated during hibernation. These adaptive mechanisms in hibernators may provide new directions to protect myocardium of non-hibernating animals, especially humans, from cardiac dysfunction induced by hypothermic stress and myocardial ischemia.

LASSO-ing Potential Nuclear Receptor Agonists and Antagonists: A New Computational Method for Database Screening

Nuclear receptors (NRs) are important biological macromolecular transcription factors that are implicated in multiple biological pathways and may interact with other xenobiotics that are endocrine disruptors present in the environment. Examples of important NRs include the androgen receptor (AR), estrogen receptors (ER), and the pregnane X receptor (PXR). In this study we have utilized the Ligand Activity by Surface Similarity Order (LASSO) method, a ligand-based virtual screening strategy to derive structural (surface/shape) molecular features used to generate predictive models of biomolecular activity for AR, ER, and PXR. For PXR, twenty-five models were built using between 8 to 128 agonists and tested using 3000, 8000, and 24,000 drug-like decoys including PXR inactive compounds (N=228). Preliminary studies with AR and ER using LASSO suggested the utility of this approach with 2-fold enrichment factors at 20%. We found that models with 64–128 PXR actives provided enrichment factors of 10-fold (10% actives in the top 1% of compounds screened). The LASSO models for AR and ER have been deployed and are freely available online, and they represent a ligand-based prediction method for putative NR activity of compounds in this database.

Agonist and antagonist binding to the nuclear vitamin D receptor: dynamics, mutation effects and functional implications

Purpose

The thermodynamically favored complex between the nuclear vitamin D receptor (VDR) and 1α,25(OH)₂-vitamin D₃ (1,25D3) triggers a shift in equilibrium to favor VDR binding to DNA, heterodimerization with the nuclear retinoid x receptor (RXR) and subsequent regulation of gene transcription. The key amino acids and structural requirements governing VDR binding to nuclear coactivators (NCoA) are well defined. Yet very little is understood about the internal changes in amino acid flexibility underpinning the control of ligand affinity, helix 12 conformation and function. Herein, we use molecular dynamics (MD) to study how the backbone and side-chain flexibility of the VDR differs when a) complexed to 1α,25(OH)₂-vitamin D₃ (1,25D3, agonist) and (23S),25-dehydro-1α(OH)-vitamin D₃-26,23-lactone (MK, antagonist); b) residues that form hydrogen bonds with the C25-OH (H305 and H397) of 1,25D3 are mutated to phenylalanine; c) helix 12 conformation is changed and ligand is removed; and d) x-ray water near the C1- and C3-OH groups of 1,25D3 are present or replaced with explicit solvent.

Methods

We performed molecular dynamic simulations on the apo- and holo-VDRs and used T-Analyst to monitor the changes in the backbone and side-chain flexibility of residues that form regions of the VDR ligand binding pocket (LBP), NCoA surface and control helix 12 conformation.

Results

The VDR-1,25D3 and VDR-MK MD simulations demonstrate that 1,25D3 and MK induce highly similar changes in backbone and side-chain flexibility in residues that form the LBP. MK however did increase the backbone and side-chain flexibility of L404 and R274 respectively. MK also induced expansion of the VDR charge clamp (i.e. NCoA surface) and weakened the intramolecular interaction between H305---V418 (helix 12) and TYR401 (helix 11). In VDR_FF, MK induced a generally more rigid LBP and stronger interaction between F397 and F422 than 1,25D3, and reduced the flexibility of the R274 side-chain. Lastly the VDR MD simulations indicate that R274 can sample multiple conformations in the presence of ligand. When the R274 is extended, the β-OH group of 1,25D3 lies proximal to the backbone carbonyl oxygen of R274 and the side-chain forms H-bonds with hinge domain residues. This differs from the x-ray, kinked geometry, where the side-chain forms an H-bond with the 1α-OH group. Furthermore, 1,25D3, but not MK was observed to stabilize the x-ray geometry of R274 during the > 30 ns MD runs.

Conclusions

The MD methodology applied herein provides an in silico foundation to be expanded upon to better understand the intrinsic flexibility of the VDR and better understand key side-chain and backbone movements involved in the bimolecular interaction between the VDR and its’ ligands.

Electronic supplementary material

The online version of this article (doi:10.1186/2193-9616-1-2) contains supplementary material, which is available to authorized users.

Role of nuclear receptors in lipid dysfunction and obesity-related diseases

This article is a report on a symposium sponsored by the American Society for Pharmacology and Experimental Therapeutics and held at the Experimental Biology 12 meeting in San Diego, CA. The presentations discussed the roles of a number of nuclear receptors in regulating glucose and lipid homeostasis, the pathophysiology of obesity-related disease states, and the promise associated with targeting their activities to treat these diseases. While many of these receptors (in particular, constitutive androstane receptor and pregnane X receptor) and their target enzymes have been thought of as regulators of drug and xenobiotic metabolism, this symposium highlighted the advances made in our understanding of the endogenous functions of these receptors. Similarly, as we gain a better understanding of the mechanisms underlying bile acid signaling pathways in the regulation of body weight and glucose homeostasis, we see the importance of using complementary approaches to elucidate this fascinating network of pathways. The observation that some receptors, like the farnesoid X receptor, can function in a tissue-specific manner via well defined mechanisms has important clinical implications, particularly in the treatment of liver diseases. Finally, the novel findings that agents that selectively activate estrogen receptor β can effectively inhibit weight gain in a high-fat diet model of obesity identifies a new role for this member of the steroid superfamily. Taken together, the significant findings reported during this symposium illustrate the promise associated with targeting a number of nuclear receptors for the development of new therapies to treat obesity and other metabolic disorders.

PXR ligand classification model with SFED-weighted WHIM and CoMMA descriptors

Understanding which type of endogenous and exogenous compounds serve as agonists for the nuclear pregnane X receptor (PXR) would be valuable for drug discovery and development, because PXR regulates a large number of genes related to xenobiotic metabolism. Although several models have been proposed to classify human PXR activators and non-activators, models with better predictability are necessary for practical purposes in drug discovery. Grid-weighted holistic invariant molecular (G-WHIM) and comparative molecular moment analysis (G-CoMMA) type 3D descriptors that contain information about the solvation free energy of target molecules were developed. With these descriptors, prediction models built using decision tree (DT)-, support vector machine (SVM)-, and Kohonen neural network (KNN)-based models exhibited better predictability than previously proposed models. Solvation free energy density-weighted G-WHIM and G-CoMMA descriptors reveal new insights into PXR ligand classification, and incorporation with machine learning methods (DT, SVM, KNN) exhibits promising results, especially SVM and KNN. SVM- and KNN-based models exhibit accuracy around 0.90, and DT-based models exhibit accuracy around 0.8 for both the training and test sets.

Evolution of promiscuous nuclear hormone receptors: LXR, FXR, VDR, PXR, and CAR

Nuclear hormone receptors (NHRs) are transcription factors that work in concert with co-activators and co-repressors to regulate gene expression. Some examples of ligands for NHRs include endogenous compounds such as bile acids, retinoids, steroid hormones, thyroid hormone, and vitamin D. This review describes the evolution of liver X receptors α and β (NR1H3 and 1H2, respectively), farnesoid X receptor (NR1H4), vitamin D receptor (NR1I1), pregnane X receptor (NR1I2), and constitutive androstane receptor (NR1I3). These NHRs participate in complex, overlapping transcriptional regulation networks involving cholesterol homeostasis and energy metabolism. Some of these receptors, particularly PXR and CAR, are promiscuous with respect to the structurally wide range of ligands that act as agonists. A combination of functional and computational analyses has shed light on the evolutionary changes of NR1H and NR1I receptors across vertebrates, and how these receptors may have diverged from ancestral receptors that first appeared in invertebrates.

Three-Dimensional Molecular Modelling of Receptor-Based Mechanisms in Toxicology

Previous chapters have discussed the generation and use of relatively simple descriptors (such as log P, topological descriptors etc) in predicting toxicity; such descriptors alone can accurately predict certain endpoints. However, other endpoints require a more complex modelling process. Molecules exist as 3-dimensional entities and where toxicity is the result of specific spatially-related interactions between the toxicant and a biological macromolecule, for example receptor-mediated effects, models must be able to take into account this 3-dimensional interaction. This chapter will present a brief overview of the use of ligand-based and receptors-based 3-dimensional approaches in toxicity prediction. An introduction to relevant software and example case studies where the approaches have been successfully employed will be presented.

Orphan nuclear receptors as targets for drug development

Orphan nuclear receptors regulate diverse biological processes. These important molecules are ligand-activated transcription factors that act as natural sensors for a wide range of steroid hormones and xenobiotic ligands. Because of their importance in regulating various novel signaling pathways, recent research has focused on identifying xenobiotics targeting these receptors for the treatment of multiple human diseases. In this review, we will highlight these receptors in several physiologic and pathophysiologic actions and demonstrate how their functions can be exploited for the successful development of newer drugs.